Tool Wear Prediction Approach for Turning Operations Based on General Regression Neural Network (GRNN) Technique E.A. Almeshaiei1, S.E. Oraby1, M. A. Mahmoud1

1College of Technological Studies, PAAET, P.O. Box 42325 Shuwaikh 70654, Kuwait. (eam@paaet.edu.kw) ABSTRACT Detection of tool failure is very important in automated manufacturing. Recent trends, being towards mostly unmanned automated machining systems and consistent system operations, need reliable on-line monitoring processes. A proper on-line cutting tool condition monitoring system is essential for deciding when to change the tool. Many methods have been attempted in this connection. Recently, artificial neural networks have been tried for this purpose because of their inherent simplicity and reasonably quick data-processing capability. The present work investigates the feasibility of using general regression neural networks (GRNN) for estimating the level of the nose wear on the cutting edge. Experimental data of different force components, as well as corresponding nose wear values and the three controlling cutting parameters (speed, feed, and depth of cut) are used to train the neural networks. The technique shows close matching of estimation of nose wear and directly measured wear value. Results indicated the need for inclusion of six input parameters (speed, feed, depth, and three force components) in order to get better prediction capability. However, some parameters, such as feeding and radial force components, have shown higher impact than others (power force component). Therefore, it is possible that the trained neural networks can accurately assess tool wear on-line using an appropriate system. 1 Introduction The need for accurate and reliable prediction of the tool wear in machining has been a long-term goal of tool manufacturers and users especially after the introduction of advanced fully automated and smart systems. Since, in-process situations, tool wear is an immeasurable parameter, indirect methods are usually used to relate its variations to visible detectable variables such as cutting forces, tool vibration, acoustic emission, cutting

temperature, etc. To be a reliable and practical tool, any proposed strategy should consider the complexity and diversity that usually characterize the nature of the machining environment. One of the methods that has been recently acknowledged to monitor tool state and condition is the intelligent discrimination of the tool abnormalities [1]. Many articles have proposed implementation of neural networks (NNs) as a means to monitor, assess and predict tool wear [2-11]. The actual measured sensor signals, cutting forces for instance, can be compared with predicted ones to determine the state of tool wear, and thus prevent damage to the tool and work piece during the course of manufacturing.

In this paper, the feasibility of using neural networks (NN) to predict different tool deformation modes in turning operation is investigated. Special attention is focused on nose wear for many reasons. It is the first failure mode to develop on the cutting edge, then it rapidly triggers the other failure modes such as flank and notch wear. Besides, it is the most controlling factor regarding the dimensional accuracy and the surface finish of the machined part.

The NN used in the analysis is of the general regression neural network (GRNN) type since this is appropriate for metal machining environment where involved variables are of continuous nature. In addition to that it is noticed that among the relevant published literature [1], very little attention was devoted to the application of prediction of continuous variables, and also since it appears that very little attention was given to the general regression neural network (GRNN). Cutting forces; feeding component Fx, radial component Fz and power component Fy, are used individually or, in different mathematical forms and ratios, as measurable sensing variables.

An artificial neural network is usually defined as a network composed of a large number of processors (neurons) that are massively interconnected, operate in parallel, and learn from experience (examples). 2 NEURAL NETWORK ALGORITHM

The general regression neural network (GRNN) is a one-pass learning algorithm that can be used for estimation of continuous variables, and converges to the underlying regression surface. The principal advantages of the GRNN are its quick learning and fast convergence to optimal regression surface as the number of samples becomes large. The overall block diagram of the GRNN in its adaptive form is shown in Fig. 1. to show a feed forward network that can be used to estimate a vector Y from a measured vector X. The input units are merely distribution units, which provide the (scaled) measured variables X to all of the neurons on the second layer, which contains the pattern units. Each pattern unit (neuron) is dedicated to one exemplar (pattern) or one cluster center. When a new vector X is entered into the network, it is subtracted from the stored vector representing each cluster center. The squares of the differences are summed and fed into a nonlinear activation function. The activation function used herein is

logistic in the form: ( ) ,1

1 xexf −−= where x is the input. This function is the most

popular and has been found useful for most network applications [12]. The pattern units’ output is passed on to the summation units. Details of the GRNN paradigm were provided by Specht [13].

Figure 1: Block diagram of a general regression NN The network "learns" by adjusting the interconnection weights between

layers. The answers the network is producing are repeatedly compared with the correct answers, and each time the connecting weights are adjusted slightly in the direction of the correct answers. Eventually, if the problem is learned, a stable set of weights adaptively evolves which will provide good answers for all of the sample predictions. The real test of neural networks occurs when the trained network is able to produce good results for new data. In this study, over-training was prevented using a so-called Net-Perfect algorithm [14]. This algorithm optimizes the network by applying the current network to an independent test set during training. The algorithm finds the optimum network for the data in the test set (which means that the network is able to generalize well and give good results on new data); the algorithm optimizes the smoothing factor based upon the values in the test set. It does this by trying different smoothing factors and choosing the one that minimizes the mean squared error between the actual and predicted answers. General regression neural networks (GRNN) work by measuring how far a given sample pattern is from patterns in the training set in N dimensional space, where N is the number of inputs in the problem. In this study, the method of measuring the distance between patterns was the so-called City Block distance metric, which is the sum of the absolute values of the differences in all dimensions between the pattern and the weight vector for that neuron [13].

)('ˆ xy

)('ˆ xy

x1 x1

x2 x2

Input Units Input Units

Pattern Units Pattern Units Summation

Units Summation Units

Output Units Output Units

f(x)K f(x)K

Kxfy )(ˆ

Kxfy )(ˆ

)(ˆ xy )(ˆ xy

The GRNN used in this study was genetic adaptive, i.e. it uses a genetic algorithm to find input smoothing factor adjustment. This is used to adapt the overall smoothing factor to provide a new value for each input. Genetic algorithms use a “fitness” measure to determine which of the individuals in the population survive and reproduce [15]. The measure of fitness for GRNN is the mean squared error of the outputs for the entire data set. The genetic adaptive algorithm seeks to minimize this mean squared error.

When variables are loaded into a neural network, they must be scaled from their numeric range into the numeric range that the neural network deals with efficiently. There are two main numeric ranges the networks commonly operate in: zero to one denoted [0, 1], and minus one to one denoted [–1, 1]. One choice is the use of linear scaling functions for this purpose. Two possible alternatives to these linear scaling functions are two non-linear scaling functions: logistic and tanh. The logistic function scales data to (0,1) according to the following formula: f(x) =1/(1+exp(–xm/s)) where xm is the average of all of the values of that variable in the pattern file, and s is the standard deviation of those values. The hyperbolic tangent function (tanh) scales data to (–1, 1) according to: f(x)=tanh(xm/s). As detailed later, a parametric study was conducted to select the best scaling function for the present application. 3 EXPERIMENTAL SETUP In order to use neural networks, comprehensive experimental testing procedures were conducted to record nose wear variations in relation to the employed cutting conditions (speed V, feed F and depth of cut D). The corresponding force signals are in-process simultaneously recorded in three directions: feeding component Fx parallel to work part axis, radial component Fz normal to work part axis and power (vertical) component Fy tangential to work rotation.

Central Composite Design (CCD) experimental design technique was used to arrange five levels of each of the operating parameters: lowest, low, moderate, high and highest. This led to 24 experiments listed in Table 1.

Table 1 CCD of the experimental procedures

Test Speed, V (m/min)

Feed, F (mm/rev)

Depth, D (mm) Test Speed, V

(m/min) Feed, F

(mm/rev) Depth, D

(mm) 1 72 0.12 2 13 206 0.2 2.25 2 145 0.3 2 14 50 0.2 2.25 3 145 0.12 2.5 15 104 0.6 2.25 4 72 0.3 2.5 16 104 0.06 2.25 5 104 0.2 2.25 17 104 0.2 3 6 104 0.2 2.25 18 104 0.2 1.5 7 145 0.12 2 19 206 0.2 2.25 8 72 0.3 2 20 50 0.2 2.25 9 72 0.12 2.5 21 104 0.6 2.25 10 145 0.3 2.5 22 104 0.06 2.25 11 104 0.2 2.25 23 104 0.2 3 12 104 0.2 2.25 24 104 0.2 1.5

Each experiment consisted of two-minute subtest intervals until nose wear on the cutting edge has reached a criterion level of 0.3 mm or, whenever the edge has catastrophically failed due to plastic deformation. This led to 669 total number of patterns used in the NN analysis. 4 RESULTS AND DISCUSSION Out of the 669 total number of patterns used in the NN analysis, the data used for training and testing the NNs (the Pattern set) were 564 patterns. The patterns in this database were divided into two sets. The first set consisted of 452 patterns and was used for training the networks. The second set consisted of the remainder 112 patterns, selected randomly, and was used for testing the trained networks. To evaluate the robustness of the networks, the trained networks are applied to a “production” data set (105 patterns) that the networks have never "seen" before.

The input layer to the neural network consisted of 6 neurons to which the speed V, the feed F, the depth of cut D, the feeding component Fx, the power component Fy, the radial component Fz were fed. The hidden layer must contain a minimum of one neuron for each data pattern; the number was set to 452. The number of neurons in the output layer is 1, which corresponds to the output (the nose wear). The statistical indicator used to evaluate the closeness of fit is the coefficient of multiple determinations, R that can be defined as [14]:

( )( )∑

∑−

−−= 2

22 1

m

p

yyyy

R where y is the actual value, yp is the predicted value of y, and ym is

the mean of the y values. The coefficient of multiple determinations, R compares the accuracy of the model to the accuracy of a trivial benchmark model wherein the prediction is simply the mean of all of the samples. A perfect fit would result in an R value of 1 and a very good fit near 1. At lower R-values, the quality of fit deteriorates. Table 2 shows that R for the Pattern and Production sets for this network are high, which is a proof of very good fit.

The results of a parametric study of the network design intended to quantitatively determine the general regression neural networks (GRNN) design (using a genetic adaptive algorithm) that best predicts the ‘production data’ are summarized in Table 2. The variables investigated included: (a) different scaling functions (linear between [–1,1], linear between [0,1], logistic, and hyperbolic tangent “tanh”), and (b) two possible ways to measure the distance between patterns, namely the City Block distance metric [13] and the Euclidean distance metric [16].

From the table it is evident that: (a) in most cases, the City Block distance metric is better than the Euclidean metric; (b) the tanh scaling function is better with the Euclidean distance metric than with the City Block; and, (c) the linear scaling functions are better with the City Block distance metric than with the Euclidean.

Based on these findings, the optimum design of the GRNN for the present application is one that uses a genetic adaptive algorithm, the City-Block distance metric, and a linear scaling function between [–1,1] for the input data. An overall comparison of the NN nose wear prediction with experimental values for the ‘Production set’ (NN Pro) (105 data patterns) is shown in figure 2. At lower levels of nose wear of 0.02 to 0.18 mm, corresponding NN estimates seem to be over estimated. However, except for few outliers, a reasonable correlation is noticed. This is expected since within that domain tool wear is usually of a stochastic nature. Fortunately, however, that wear level is too low to be considered.

Comparisons between NN estimates and corresponding experimental data for individual tests are found in a good agreement as shown in Figs. 3,4. Correlation may be discussed in the light of machining performance due to level of operating conditions (V,F,D) used in experiment. While low-to-moderate speed (V) level of (72-104 m/min) was employed in experiments 1,8,9 and 14, Fig. 3, higher level was used in experiments 17,18,23 and 24, Fig. 4. While wider scatter is noticed in the former set, better correlation is shown for the later set. Higher speed usually invokes higher wear rate with more stable machining that is free of edge chipping and built-up edge mechanism. This may explain results in Fig. 2 where, at low wear values, NN estimates are over estimated. However, at a given speed level, experiments 1,8 and 9, Fig. 3, higher feed (F) value seems to degrade NN predictability. This is thought to be due to the conditions discussed above. Depth of cut (D), however, tends to positively influence correlation where deeper value leads to more stable operation and, accordingly, improves estimation accuracy (compare experiments 1 and 9, Fig. 3). However, it may be concluded that error between experimental and NN estimates is of acceptable level and distribution pattern especially with the well known stochastic nature that characterizes machining operations. Additionally, results showed a better predictability over the conventional multiple or non-linear regression techniques as was previously investigated by one of the authors [17].

Table 2 R values for various network designs; inputs: V, F, D, Fx, Fy, Fz

Distance Metric Scaling Function R for Pattern set

R for Production set

City Block Linear [0,1] 0.9811 0.9261 City Block Linear [–1,1] 0.9745 0.9280 City Block Logistic 0.9774 0.9269 City Block Tanh 0.9394 0.9066 Euclidean Linear [0,1] 0.9255 0.8624 Euclidean Linear [–1,1] 0.9143 0.8390 Euclidean Logistic 0.9529 0.9159 Euclidean Tanh 0.9434 0.9142

As pointed out in the introduction, there is a need to include (as inputs to the NN) more than one cutting parameter. In the foregoing discussion, the control parameters (S, F, D) and the three cutting forces were considered. An objective of the present study was to quantitatively determine which combination of these parameters gives best wear prediction. Therefore, a total of 14 possibilities and combinations were considered as shown in Table 3. For each case, 8 different NN designs were evaluated in a manner similar to Table 2, and the best design is found. Due to the emerged outcome, it may conclude that:

• for all cases, the City Block distance metric should be used in the NN design rather than the Euclidean, • a linear scaling function is useful in most cases, • the use of all three force components, or their ratios in association with the cutting control parameters (S, F, D) give best wear predictions, • the use of only cutting force components, without the cutting control parameters, dramatically deteriorates the NN performance as pointed out by case 12, • the two most significant force components to trace the nose wear are Fx and Fz as pointed out by data of case 4. This agrees with the technical fact that tool waer, especially nose scars, is well correlated to frictional force components (Feeding Fx and Radial Fz) rather that to the power (vertical) component Fy, and • among the three force components, feeding component Fx shows most correlation (case 6) while the least is found for the power component Fy (cases 13).

Figure 2: NN nose wear prediction vs experimental values; Production set.

0 0.1 0.2 0.3 0.40

0.1

0.2

0.3

0.4

Experimental, mm

NN

, mm

Figure 3: Comparison of Neural and Experimental estimates.

0 50 1000.1

0.2

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0.4 experiment # 8 Pat Pro

Time, minute

0.0

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wea

r, m

mNo

se w

ear,

mm

experiment # 9 Pat Pro

0 50 100 1500.1

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0.4 experiment # 1 Pat Pro

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wea

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m

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0.6 experiment # 14 Pat Pro

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wea

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0 20 40 600.1

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experiment # 18 Pat Pro

Time, minute

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experiment # 24 Pat Pro

0 10 20 300.1

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m

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experiment # 23 Pat Pro

Nose

wea

r, m

m

Figure 4: Comparison of Neural and Experimental estimates.

Table 3 R for various networks and best design (Fxz= √(Fx2 + Fz

2) , Px = S Fx )

Input Variables Pattern Data Production Data

Best Network Design

1 Fx Fy Fz V F D 0.9745 0.9280 City Block, -1,1 2 Fx/Fy Fz/Fy V F D 0.9666 0.9243 City Block, -1,1 3 Px Py Pz V F D 0.9580 0.9233 City Block, Logistic 4 Fx Fz V F D 0.9652 0.9222 City Block, Logistic 5 Px Py Pz F D 0.9728 0.9200 City Block, -1,1 6 Fx V F D 0.9141 0.9043 City Block, -1,1 7 Fxz V F D 0.9553 0.9023 City Block, -1,1 8 Px/Py Pz/Py F D 0.9666 0.9008 City Block, 0,1 9 Fz V F D 0.9521 0.8953 City Block, -1,1 10 Fxz Fy V F D 0.9637 0.8780 City Block, -1,1 11 Fx/ Fz Fy F D 0.9575 0.8756 City Block, -1,1 12 Fx Fy Fz Only 0.9738 0.8697 City Block, -1,1 13 Fy V F D 0.9236 0.7792 City Block, 0,1 14 Fx/ Fz V F D 0.8953 0.7556 City Block, -1,1

5 CONCLUSIONS Signals of different cutting force components usually show a remarkable functional correlation with the developed wear scars on the nose of the cutting tool when rough turning is performed. This was investigated by many researches using various mathematical and formulation techniques. Preference of one technique over others is always related to its capability to be used in establishing an online monitoring of the operation especially cutting tool performance. Actual in-process collected force signals from the machining system are fed to the established trained NN model and, technical decision is taken accordingly, Fig. 5.

In this work, neural network are used as an applicable intelligent system toward the establishment of a reliable cutting tool condition monitoring using cutting force signals. Different neural routines are investigated. Inputs to the system are the controlling cutting parameters (Speed, feed, and depth of cut) and three components of force signals (feeding, radial and vertical). Output from the system is an estimate level of nose wear on the cutting edge. Among emerged conclusions and concluding remarks are:

• Nose wear is given a special attention in the study and in tool condition monitoring, in general, since it directly affect both the dimensional accuracy and the surface quality of the machined surface. • Also, It is found that the most appropriate GRNN design for the data involved is the one that uses a genetic adaptive algorithm with a City-Block distance metric and a linear scaling function between [-1,1]. • The use of all the six neurons (parameters) mentioned above gives best prediction of the nose wear. Less performance is found when either set of cutting parameters or set of force components is used independently.

Fx Fy Fz

Trained NN Model

(V,F

,D)

• Among the set of cutting force components, the use of feed and/or radial components showed better predictability than the use of power component.

Figure 5: Illustrative diagram of a Tool Wear Monitoring and Control Technique

Machining System

Tool State

Diagnostic & Action

Low Wear (Go On)

Change Tool

Modify Condition

REFERENCES 1 Dimla JR DE, Lister PM and Leighton NJ, (1997) Neural Network Solutions

to the Tool Condition Monitoring Problem in Metal Cutting – A Critical Review of Methods, Int. J. Mach. Tools & Manuf., 30 (29), 1219-1241.

2 Dimla JR DE, Lister PM and Leighton NJ, (1998) Automatic Tool State Identification in a Metal Turning Operation using MLP Neural Networks and Multivariate Process Parameters, Int. J. Mach. Tools & Manuf., 38 (4), 343-352.

3 T. Luo T, Lu W, Krishnamurthy K and McMillan B, (1998) A Neural Network Approach for Force and Contour Error Control in Multi-Dimensional End Milling Operations, Int. J. Mach. Tools Manuf., 38 (10-11), 1343-1359.

4 Stone R and Krishnamurthy K, (1996) A Neural Network Thrust Force Controller to Minimize Delimitation During Drilling of graphic-Epoxy Laminates, Int. J. Mach. Tools Manuf., 36 (9), 985-1003.

5 Yeh Zong-Mu, Tarang Y. S. and Nian C. Y., (1995) A Self-Organizing Neural Fuzzy Logic Controller for Turning Operations, Int. J. Mac. Tools Manuf., 35 (10), 1363-1374.

6 Shang-Liang Chen and Jen YW, (2000) Data Fusion Neural Network for Tool Condition Monitoring in CNC Milling Machining, Int. J. Mach. Tools Manufact., 40 (3), 381-400.

7 Lin CS. and Ting JC., (1996) Drill Wear Monitoring using Neural Networks, Int. J. Mach. Tools Manuf., 36 (4), 465-475.

8 Das S, Roy R, and Chattopadhyay AB, (1996) Evaluation of Wear Turning Carbide Inserts using Neural Networks, Int. J. Mach. Tools Manuf., 36 (7), 789-797.

9 Kim TY, Woo J, Shin D and Kim J, (1999) Indirect Cutting Force Measurement in Multi-Axis Simultaneous NC Milling processes, Int. J. Mach. Tools Manuf., 39 (11), 1717-1731.

10 Raj KH, Sharma RS, Srivastava S and Patvardhar C, (2000) Modeling of Manufacturing Processes with ANNs for Intelligent Manufacturing, Int. J. Mach. Tools Manuf., 40 (6), 851-868.

11 Scheffer C, Kratz H, Heyns PS, and Klocke F, (2003) Development of a Tool Wear-Monitoring System for Hard Turning, Int. J. Mach. Tools Manuf., 43, 973-985.

12 Hammerstrom D, (July 1993) Working with Neural Networks, IEEE Spectrum, 46-53.

13 Specht DF, (1991) A General Regression Neural Network. IEEE Transactions on Neural Networks, 2 (6), 568-576.

14 Neuroshell 2 manual, (1996) Ward Systems Group Inc., Frederick, Mass. 15 Goldberg DE, (1989) Genetic Algorithms in Search Optimization, and

Machine Learning. Addison-Wesley, Reading, MA. 16 Kreyszig E, (1993) Advanced Engineering Mathematics, Seventh Edition,

Wiley, New York. 17 Oraby SE and Hayhurst DR, (1991) Development of Models for Tool Wear

Force Relationships in Metal Cutting, Int. J. Mech. Sci., 33 (2), 125-138.